Speakers
Description
Muons produced in W-boson and heavy-flavour (HF) hadron decays provide useful information on particle production in proton-proton (pp) collisions. Both mother particles have large masses and are produced in hard-parton scattering in the initial stages of the collision. W-boson studies can probe light-quark parton distribution functions and serve as a baseline for the study of initial-state nuclear effects. HF measurements in pp collisions help to understand the heavy-quark production mechanisms, fragmentation, hadronisation, and are an important test of perturbative Quantum Chromodynamics. The ALICE detector is uniquely equipped to probe HF and electroweak processes via the single-muon decay channel across both low- and high-transverse momentum probing small Bjorken-$x$ dynamics.
In this contribution, realistic simulations of pp collisions at $\sqrt{s}=13.6\,\text{TeV}$ are used to determine the two-particle correlations of forward-rapidity ($2.5 < y < 4.0$) single muons from $\text{B}$-hadron decays with charged particles ($|y| < 0.8$) to study the hadronisation mechanism of $\text{b}$ quarks. Machine Learning (ML) techniques, specifically neural networks, are used at low $p_{\text{T}}$ ($p_\text{T} < 20\,\text{GeV}/c$) to separate HF semileptonic decay muons ($\text{HF} \to \mu$) from background candidates. The ML technique is extended to high $p_{\text{T}}$ ($p_\text{T} > 20\,\text{GeV}/c$) to isolate electroweak $\text{W} \to \mu\nu$ signals from HF and $\text{Z} \to \mu^+\mu^-$ background contributions.
